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Keywords

cyber-physical power systems; load-frequency control; Kalman filter; hybrid attacks; attack detection

Abstract

Against the backdrop of extensively interconnected cyber-physical power systems (CPPS), this paper proposes an attack detection method based on an enhanced Kalman filter to address hybrid attacks involving denial-of-service (DoS) attacks and false data injection (FDI) attacks. First, a load-frequency control model is established for a two-region interconnected power system, and a hybrid attack model incorporating both DoS and FDI attacks is constructed. Second, an enhanced Kalman filter is introduced, incorporating an adaptive process noise adjustment mechanism and a residual history sliding window to enhance the robustness of state estimation. Subsequently, an improved attack detector is designed, integrating chi-square detection, adaptive threshold adjustment, and sliding window cumulative detection strategies to achieve rapid attack identification and classification. The Kalman filter handles system state estimation and generates a residual sequence, while the attack detector performs statistical hypothesis testing based on the residual sequence, identifying attacks by monitoring abnormal residual variations. Finally, the Kalman filter parameters are optimized using a simulated annealing algorithm to enhance the system's frequency stability and state estimation accuracy under attack conditions. Simulation results demonstrate the effectiveness and superiority of the proposed method in improving CPPS hybrid attack detection.

DOI

10.19781/j.issn.1673-9140.2026.02.007

First Page

79

Last Page

88

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